Introduction
Artificial Intelligence (“AI”) has been defined as the study of “rational agents” capable of perceiving their environment and taking actions to achieve defined objectives[i]. AI has moved beyond academic theory and is now perceived as powerful driver of change by influencing how businesses operate, public services are delivered, and everyday decisions are made across the globe. The rapid adoption of AI has also created governance challenges which the organizations are only beginning to reckon with. AI systems are now generating results that may be discriminatory, potentially harmful, and difficult to explain.
AI Governance describes the legal, ethical, and operational framework used to govern the design, development, deployment and monitoring of AI systems to ensure that these systems are implemented and operated with responsibly, transparency, and reliably. This framework also helps organisations comply with relevant laws when using AI while addressing issues such as fairness, accountability, transparency, privacy, security and human oversight. AI Governance must address not only technical performance but also the social and institutional conditions under which AI systems operate, thereby making it as much a legal and organizational challenge as a technological one. As AI gets deeply embedded across industries -from business operations and customer engagement to decision-making processes-, regulators, boards of directors, investors and consumers are placing growing pressure on the organizations to develop robust AI Governance measures. solutions.
The global regulatory environment also reflects this shift. The European Union AI Act[ii] establishes a tiered, risk-based compliance for AI-based systems. In India, the Ministry of Electronics and Information Technology has emphasized on adopting AI governance principles such as fairness, accountability, transparency, and safety[iii]. In the United States, the Federal Trade Commission has signaled active enforcement against deceptive or unfair AI-driven practices.[iv]
Yet most the companies deploying AI systems today are not large technology companies with dedicated in-house legal, governance and compliance functions. These companies are often mid-sized and sector -specific businesses that require senior legal guidance without the financial burden of a full-time General Counsel.
When an AI system produces discriminatory outcomes, mishandles personal data, or generates content that infringes third-party intellectual property rights, legal and regulatory responsibility essentially falls on the organizations, and in some cases, its founders, or officers.
Beyond regulatory risks, AI governance is also becoming increasingly relevant from an investor due diligence perspective. The Institutional and venture investors are keen to evaluate whether an organization has taken adequate and demonstrable steps to identify, manage, and mitigate the legal risks associated with its AI systems before committing capital.
The Fractional General Counsel (“Fractional GC”) model addresses this gap by giving access to embedded legal support on a part-time or project-specific basis to help them manage the growing legal and governance complexities arising from AI deployment.
This article examines the key legal issues arising from AI deployment, the role of the Fractional GC in managing organisational AI risk, and the governance frameworks that businesses need to develop to deploy AI responsibly and in line with evolving legal requirements.
The Foundational Architecture of AI Governance
The foundational pillars of AI Governance constitute the structural framework through which organisations support responsible development, deployment and monitoring of AI systems. These pillars commonly include transparency, accountability, fairness, privacy and data protection, security, human oversight and regulatory compliance. They collectively define the legal and institutional architecture that responsible AI deployment must satisfy[v].
Transparency requires that the organisations ensure adequate explainability about how their AI systems work and make decisions, particularly where materially affect individuals. Accountability in AI ensures that an identifiable person or body within the organisation bears responsibility for the AI-driven outputs thereby preventing an organisation from having unregulated and autonomous decision-making. The Fairness Pillar requires the active identification and the mitigation of algorithmic bias and preventing bias-based outcomes. Privacy and Data Protection Pillars oblige the organisation to handle personal and sensitive forms of data in accordance with the laws and ethical standards applicable to those data types. The Security AI Pillar focuses on protecting AI systems from cyber threats, manipulation and abuses. The Human Oversight AI Pillar requires meaningful human review and a decision-making role for human beings when high-impact decisions impacted by Artificial Intelligence. The Regulatory Compliance AI Pillar focuses on ensuring that AI practices are consistent with evolving legal frameworks, industry norms and ethical obligations for AI, domestically and internationally.
Collectively, these pillars represent the governance architecture through which organisations can demonstrate that their use of AI is transparent, accountable and legally and ethically compliant. It is the operationalisation of this architecture that makes embedded legal counsel not a peripheral consideration but a governance necessity.
What is Fractional General Counsel? Structure, Mandate, and Differentiation
A Fractional GC is a lawyer who provides legal counsel as a general counsel to a business on a part-time or project basis. Instead of hiring an outside attorney for a specific matter, the Fractional GC is integrated part of the leadership team of the organization and can attend board meetings, provide strategic counsel, manage vendor relationships, and develop internal legal capability without creating the expense of full-time employment.
The Fractional GC model differs from the traditional attorney-client relationship in three significant respects. First, while outside counsel typically advises on a single legal question at a time, the Fractional GC participates in business decisions from the outset, enabling initiative-taking identification of legal risks before they materialize. Second, outside transactional counsel may not always acquire the same degree of institutional knowledge of the organization’s operations, culture, and risk profile that a Fractional GC develops over time – knowledge that enables more systematic and contextually informed risk management. Third, the model provides organizations with access to partner-level or general counsel-equivalent expertise at a fraction of the cost of a full-time hire.[vi]
The Fractional GC plays a unique and important advisory role in considering AI governance. Decisions regarding the use, purchase and implementation of AI technology are as much about legality as they are about technicality. Each decision involves legal obligations for processing, licensing of intellectual property, liability, and compliance with regulations. Fractional GC is in an excellent position, at the interface of strategy and law, to help ensure that AI decisions are made correctly before they are finalized.
AI Governance Domains: The FGC’s Legal Portfolio
Data Protection and Privacy
AI systems are inherently data-intensive and their deployment directly implies the legal obligations governing data collection, processing, and use. In India, the Digital Personal Data Protection Act 2023[vii] (“DPDP Act”), establishes a consent-based framework for processing personal data, with obligations around purpose limitation, data minimization, and data fiduciary accountability.[viii] For organizations processing data relating to EU citizens, the GDPR[ix] the GDPR places restrictions on solely automated decision-making that produces legal or similarly significant effects, and provides data subjects with related rights.
The Fractional GCs role in this domain encompasses review of data processing agreements, ensuring compliance with consent requirements for data processing, and advising regarding data principal rights in relation to trained models.
Algorithmic Accountability and Bias
The most often overlooked legal risk associated with AI and machine learning is the possibility of biased algorithms. This is a situation in which an AI or ML model makes an error in its output that puts particular types of people at a disadvantage. Bias-related issues in AI are not just a matter of ethical consideration; they also expose businesses to liability under anti-discrimination laws, consumer protection laws, and regulatory compliance requirements in regulated industries, as well as sector-specific equal treatment obligations.[x]
The Fractional GC’s role in this domain is both preventive and responsive. Prior to deployment, the Fractional GC advises on bias testing requirements, negotiates contractual obligations requiring vendors to conduct and disclose algorithmic audits, and reviews technical documentation for adequate bias disclosures. During deployment, the Fractional GC establishes internal bias monitoring protocols and maintains the documentation necessary to demonstrate regulatory diligence.
Contractual Risk and AI Procurement
Most organizations deploy third-party vendors- cloud AI platforms, specialised model providers, or embedded AI features in enterprise software. These procurement relationships are governed by contracts that frequently favor the vendor’s interests and include material limitations of liability, broad disclaimers of warranty, and permissive data-use terms.[xi]
Fractional GC’s procurement role is both preventive and structural. Prior to execution, the Fractional GC reviews vendor terms to identify unfavorable clauses, negotiates liability caps commensurate with the organization’s risk exposure, and secures data use restrictions preventing vendors from exploiting organizational data for model training without consent. During the vendor relationship, the Fractional GC monitors compliance with negotiated terms and advises on contract variations as AI systems are updated or expanded. produced during the relationship.
Fractional GC as AI Governance Architect
In addition to providing reactive legal advice, Fractional GC’s greatest contribution to AI governance is in establishing an institutional framework. in addition to providing reactive legal advice, the Fractional GC’s key contribution lies in helping organisations build a practical AI governance framework. This includes supporting internal policies, vendor governance, data protection processes, human oversight mechanisms, and reporting structures for management or investor review.
By working across legal, business, product, and compliance functions, the Fractional GC helps ensure that AI adoption is not informal, fragmented, or purely technology led. Instead, AI-related decisions are reviewed through a structured legal and governance lens before they create regulatory, contractual, reputational, or operational exposure.
In this sense, the Fractional GC acts not merely as an adviser on individual legal issues, but as an architect of the organisation’s AI governance infrastructure- helping translate legal obligations and commercial risks into practical systems of accountability.
Conclusion
AI governance is no longer a peripheral legal consideration. At its core, AI Governance presents a challenge within the legal framework. The legal risks arising from AI deployment span data protection, algorithmic accountability, intellectual property, and contractual exposure, and they are compounded by an evolving multi-jurisdictional regulatory environment in which the consequences of non-compliance are increasingly significant.
The Fractional GC provides legal advice to organizations for specific legal questions related to AI governance and also contributes to the development of the institutional infrastructure needed to allow for responsible deployment of AI and for organizations to demonstrate their responsibility to regulators and stakeholders. Organizations that have invested in this governance infrastructure are already better positioned to respond to enforcement under the DPDP Act, scrutiny under the GDPR, and sector-specific obligations imposed by the RBI, SEBI, and IRDAI.
The time to build institutional capacity is now and not after comprehensive regulation has been enacted for AI, Therefore building institutional capacity can be accomplished using structures like the Fractional GC to provide legal accountability in time to keep pace with technological advancements and capability.[xii]
References:
[i] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (4th end, Pearson 2021) 1
[ii] Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonized rules on artificial intelligence (Artificial Intelligence Act) [2024] OJ L 2024/1689.
[iii] Ministry of Electronics and Information Technology (MeitY), Responsible AI for All: Adopting the Framework – A Use Case Approach for Governance of AI in India (Government of India 2021) 6–9.
[iv] Federal Trade Commission, ‘Aiming for Truth, Fairness, and Equity in Your Company’s Use of AI’ (FTC Business Blog, April 2021) accessed 19 May 2026.
[v] High-Level Expert Group on Artificial Intelligence, Ethics Guidelines for Trustworthy AI (European Commission 2019) 14–17; OECD, Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449, updated 2024); see also Luciano Floridi and others, ‘An Ethical Framework for a Good AI Society’ (2018) 28(4) Minds and Machines 689, 693.
[vi] Rees Morrison, ‘The Rise of the Fractional General Counsel’ (2023) 35 Corporate Counsel Review 112; Association of Corporate Counsel, Chief Legal Officer Survey 2024 (ACC 2024) 34.
[vii] Digital Personal Data Protection Act 2023, No 22 of 2023.
[viii] Digital Personal Data Protection Act 2023 (India), ss 4–7 (consent obligations), s 11 (right to erasure), s 8 (obligations of data fiduciaries).
[ix] Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016, General Data Protection Regulation, OJ L 119/1, 4 May 2016.
[x] See Solon Barocas and Moritz Hardt, ‘Fairness in Machine Learning’ (NeurIPS Tutorial, 2017); Rashida Richardson, Jason Schultz and Kate Crawford, ‘Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice’ (2019) 94 New York University Law Review Online192.
[xi] See World Commerce and Contracting, AI Contracting: Risk and Liability Allocation in Technology Agreements (WCC, 2024); Clifford Chance, ‘Contracting for AI: Key Legal Issues’ (Client Briefing, March 2024) accessed 11 April 2026.
[xii] For the proposition that initiative-taking governance investment yields regulatory advantage, see Cary Coglianese and David Lehr, ‘Regulating by Robot: Administrative Decision Making in the Machine-Learning Era’ (2017) 105 Georgetown Law Journal 1147, 1195–97.
Gaurav Gupta is the Founder and Managing Partner at Bridge Counsels & Bhavya Sanadhya is a 5th year Student at School of Law, Christ University and a legal trainee at Bridge Counsels.
